Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations866
Missing cells30
Missing cells (%)0.2%
Duplicate rows19
Duplicate rows (%)2.2%
Total size in memory128.7 KiB
Average record size in memory152.2 B

Variable types

Numeric13
Categorical6

Alerts

Dataset has 19 (2.2%) duplicate rowsDuplicates
Odds is highly overall correlated with team and 1 other fieldsHigh correlation
Season is highly overall correlated with highest_salary and 1 other fieldsHigh correlation
avg_age is highly overall correlated with avg_expHigh correlation
avg_exp is highly overall correlated with avg_ageHigh correlation
avg_height is highly overall correlated with avg_weightHigh correlation
avg_weight is highly overall correlated with avg_heightHigh correlation
conference is highly overall correlated with team and 1 other fieldsHigh correlation
highest_salary is highly overall correlated with Season and 1 other fieldsHigh correlation
nb_champ_past_4y is highly overall correlated with winnerHigh correlation
nb_championships is highly overall correlated with nb_po_apperence and 2 other fieldsHigh correlation
nb_po_apperence is highly overall correlated with nb_championships and 2 other fieldsHigh correlation
team is highly overall correlated with Odds and 4 other fieldsHigh correlation
team_full_name is highly overall correlated with Odds and 4 other fieldsHigh correlation
total_salary is highly overall correlated with Season and 1 other fieldsHigh correlation
winner is highly overall correlated with nb_champ_past_4yHigh correlation
nb_champ_past_4y is highly imbalanced (70.9%)Imbalance
winner is highly imbalanced (79.4%)Imbalance
ranking has 30 (3.5%) missing valuesMissing
nb_championships has 446 (51.5%) zerosZeros

Reproduction

Analysis started2024-10-03 08:55:38.247891
Analysis finished2024-10-03 08:55:45.949605
Duration7.7 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Season
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.291
Minimum2007
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:45.979122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2008
Q12010
median2013
Q32018
95-th percentile2024
Maximum2025
Range18
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.1390683
Coefficient of variation (CV)0.0025513038
Kurtosis-0.80438594
Mean2014.291
Median Absolute Deviation (MAD)3
Skewness0.60905179
Sum1744376
Variance26.410023
MonotonicityNot monotonic
2024-10-03T16:55:46.017719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2010 90
 
10.4%
2011 90
 
10.4%
2012 90
 
10.4%
2008 59
 
6.8%
2009 59
 
6.8%
2013 59
 
6.8%
2014 59
 
6.8%
2025 30
 
3.5%
2015 30
 
3.5%
2016 30
 
3.5%
Other values (9) 270
31.2%
ValueCountFrequency (%)
2007 30
 
3.5%
2008 59
6.8%
2009 59
6.8%
2010 90
10.4%
2011 90
10.4%
2012 90
10.4%
2013 59
6.8%
2014 59
6.8%
2015 30
 
3.5%
2016 30
 
3.5%
ValueCountFrequency (%)
2025 30
3.5%
2024 30
3.5%
2023 30
3.5%
2022 30
3.5%
2021 30
3.5%
2020 30
3.5%
2019 30
3.5%
2018 30
3.5%
2017 30
3.5%
2016 30
3.5%

team
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
ATL
 
29
MIA
 
29
IND
 
29
HOU
 
29
DET
 
29
Other values (30)
721 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2598
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowATL
2nd rowATL
3rd rowATL
4th rowATL
5th rowATL

Common Values

ValueCountFrequency (%)
ATL 29
 
3.3%
MIA 29
 
3.3%
IND 29
 
3.3%
HOU 29
 
3.3%
DET 29
 
3.3%
LAC 29
 
3.3%
DEN 29
 
3.3%
DAL 29
 
3.3%
CLE 29
 
3.3%
CHI 29
 
3.3%
Other values (25) 576
66.5%

Length

2024-10-03T16:55:46.056739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atl 29
 
3.3%
bos 29
 
3.3%
nyk 29
 
3.3%
orl 29
 
3.3%
phi 29
 
3.3%
pho 29
 
3.3%
por 29
 
3.3%
uta 29
 
3.3%
sas 29
 
3.3%
tor 29
 
3.3%
Other values (25) 576
66.5%

Most occurring characters

ValueCountFrequency (%)
A 281
 
10.8%
O 237
 
9.1%
L 232
 
8.9%
S 176
 
6.8%
I 174
 
6.7%
N 171
 
6.6%
C 170
 
6.5%
H 159
 
6.1%
M 145
 
5.6%
E 118
 
4.5%
Other values (11) 735
28.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2598
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 281
 
10.8%
O 237
 
9.1%
L 232
 
8.9%
S 176
 
6.8%
I 174
 
6.7%
N 171
 
6.6%
C 170
 
6.5%
H 159
 
6.1%
M 145
 
5.6%
E 118
 
4.5%
Other values (11) 735
28.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2598
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 281
 
10.8%
O 237
 
9.1%
L 232
 
8.9%
S 176
 
6.8%
I 174
 
6.7%
N 171
 
6.6%
C 170
 
6.5%
H 159
 
6.1%
M 145
 
5.6%
E 118
 
4.5%
Other values (11) 735
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 281
 
10.8%
O 237
 
9.1%
L 232
 
8.9%
S 176
 
6.8%
I 174
 
6.7%
N 171
 
6.6%
C 170
 
6.5%
H 159
 
6.1%
M 145
 
5.6%
E 118
 
4.5%
Other values (11) 735
28.3%

team_full_name
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
Atlanta Hawks
 
29
Miami Heat
 
29
Indiana Pacers
 
29
Houston Rockets
 
29
Detroit Pistons
 
29
Other values (30)
721 

Length

Max length33
Median length20
Mean length16.072748
Min length9

Characters and Unicode

Total characters13919
Distinct characters47
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAtlanta Hawks
2nd rowAtlanta Hawks
3rd rowAtlanta Hawks
4th rowAtlanta Hawks
5th rowAtlanta Hawks

Common Values

ValueCountFrequency (%)
Atlanta Hawks 29
 
3.3%
Miami Heat 29
 
3.3%
Indiana Pacers 29
 
3.3%
Houston Rockets 29
 
3.3%
Detroit Pistons 29
 
3.3%
Los Angeles Clippers 29
 
3.3%
Denver Nuggets 29
 
3.3%
Dallas Mavericks 29
 
3.3%
Cleveland Cavaliers 29
 
3.3%
Chicago Bulls 29
 
3.3%
Other values (25) 576
66.5%

Length

2024-10-03T16:55:46.095134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 70
 
3.5%
los 58
 
2.9%
angeles 58
 
2.9%
atlanta 29
 
1.5%
portland 29
 
1.5%
washington 29
 
1.5%
wizards 29
 
1.5%
hawks 29
 
1.5%
timberwolves 29
 
1.5%
toronto 29
 
1.5%
Other values (60) 1584
80.3%

Most occurring characters

ValueCountFrequency (%)
e 1180
 
8.5%
a 1155
 
8.3%
s 1114
 
8.0%
1107
 
8.0%
o 885
 
6.4%
n 861
 
6.2%
i 823
 
5.9%
r 805
 
5.8%
t 770
 
5.5%
l 749
 
5.4%
Other values (37) 4470
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10806
77.6%
Uppercase Letter 1947
 
14.0%
Space Separator 1107
 
8.0%
Decimal Number 58
 
0.4%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1180
10.9%
a 1155
10.7%
s 1114
10.3%
o 885
8.2%
n 861
 
8.0%
i 823
 
7.6%
r 805
 
7.4%
t 770
 
7.1%
l 749
 
6.9%
c 293
 
2.7%
Other values (13) 2171
20.1%
Uppercase Letter
ValueCountFrequency (%)
C 200
 
10.3%
M 174
 
8.9%
P 157
 
8.1%
S 151
 
7.8%
B 149
 
7.7%
N 128
 
6.6%
A 116
 
6.0%
H 113
 
5.8%
T 112
 
5.8%
W 87
 
4.5%
Other values (10) 560
28.8%
Decimal Number
ValueCountFrequency (%)
7 29
50.0%
6 29
50.0%
Space Separator
ValueCountFrequency (%)
1107
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12753
91.6%
Common 1166
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1180
 
9.3%
a 1155
 
9.1%
s 1114
 
8.7%
o 885
 
6.9%
n 861
 
6.8%
i 823
 
6.5%
r 805
 
6.3%
t 770
 
6.0%
l 749
 
5.9%
c 293
 
2.3%
Other values (33) 4118
32.3%
Common
ValueCountFrequency (%)
1107
94.9%
7 29
 
2.5%
6 29
 
2.5%
/ 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1180
 
8.5%
a 1155
 
8.3%
s 1114
 
8.0%
1107
 
8.0%
o 885
 
6.4%
n 861
 
6.2%
i 823
 
5.9%
r 805
 
5.8%
t 770
 
5.5%
l 749
 
5.4%
Other values (37) 4470
32.1%

conference
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
EAST
435 
WEST
431 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3464
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEAST
2nd rowEAST
3rd rowEAST
4th rowEAST
5th rowEAST

Common Values

ValueCountFrequency (%)
EAST 435
50.2%
WEST 431
49.8%

Length

2024-10-03T16:55:46.131672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T16:55:46.163727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
east 435
50.2%
west 431
49.8%

Most occurring characters

ValueCountFrequency (%)
E 866
25.0%
S 866
25.0%
T 866
25.0%
A 435
12.6%
W 431
12.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3464
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 866
25.0%
S 866
25.0%
T 866
25.0%
A 435
12.6%
W 431
12.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 3464
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 866
25.0%
S 866
25.0%
T 866
25.0%
A 435
12.6%
W 431
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 866
25.0%
S 866
25.0%
T 866
25.0%
A 435
12.6%
W 431
12.4%

avg_age
Real number (ℝ)

HIGH CORRELATION 

Distinct379
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.371051
Minimum23.210526
Maximum31.315789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:46.198692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23.210526
5-th percentile24.417763
Q125.5
median26.1875
Q327.235294
95-th percentile28.7
Maximum31.315789
Range8.1052632
Interquartile range (IQR)1.7352941

Descriptive statistics

Standard deviation1.3251528
Coefficient of variation (CV)0.050250282
Kurtosis0.23037035
Mean26.371051
Median Absolute Deviation (MAD)0.88846154
Skewness0.43732685
Sum22837.33
Variance1.7560298
MonotonicityNot monotonic
2024-10-03T16:55:46.246575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 14
 
1.6%
25.16666667 12
 
1.4%
26 10
 
1.2%
26.72222222 10
 
1.2%
25.75 9
 
1.0%
25.66666667 9
 
1.0%
26.125 9
 
1.0%
25.5 8
 
0.9%
25.76470588 8
 
0.9%
25.21052632 7
 
0.8%
Other values (369) 770
88.9%
ValueCountFrequency (%)
23.21052632 1
 
0.1%
23.30434783 2
0.2%
23.4 1
 
0.1%
23.4375 1
 
0.1%
23.60869565 1
 
0.1%
23.61111111 1
 
0.1%
23.64705882 3
0.3%
23.65217391 1
 
0.1%
23.65384615 1
 
0.1%
23.72727273 1
 
0.1%
ValueCountFrequency (%)
31.31578947 2
0.2%
31.05882353 1
 
0.1%
30.23529412 3
0.3%
30.04761905 1
 
0.1%
29.83333333 2
0.2%
29.72222222 2
0.2%
29.53333333 1
 
0.1%
29.47619048 1
 
0.1%
29.4375 1
 
0.1%
29.375 3
0.3%

avg_exp
Real number (ℝ)

HIGH CORRELATION 

Distinct366
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6321082
Minimum1.6521739
Maximum8.6470588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:46.293106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.6521739
5-th percentile2.895098
Q13.7142857
median4.4
Q35.3684211
95-th percentile7.1083333
Maximum8.6470588
Range6.9948849
Interquartile range (IQR)1.6541353

Descriptive statistics

Standard deviation1.2775474
Coefficient of variation (CV)0.27580258
Kurtosis0.29517611
Mean4.6321082
Median Absolute Deviation (MAD)0.8
Skewness0.62808798
Sum4011.4057
Variance1.6321274
MonotonicityNot monotonic
2024-10-03T16:55:46.337617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 13
 
1.5%
4.4 12
 
1.4%
4.625 11
 
1.3%
4.75 11
 
1.3%
4 11
 
1.3%
5 9
 
1.0%
3.875 9
 
1.0%
4.157894737 7
 
0.8%
4.666666667 7
 
0.8%
4.388888889 7
 
0.8%
Other values (356) 769
88.8%
ValueCountFrequency (%)
1.652173913 2
0.2%
1.727272727 1
0.1%
1.730769231 1
0.1%
1.92 1
0.1%
2.043478261 1
0.1%
2.1 2
0.2%
2.105263158 1
0.1%
2.117647059 1
0.1%
2.318181818 1
0.1%
2.368421053 1
0.1%
ValueCountFrequency (%)
8.647058824 4
0.5%
8.526315789 2
0.2%
8.380952381 1
 
0.1%
8.266666667 1
 
0.1%
8.12 1
 
0.1%
7.9375 3
0.3%
7.875 3
0.3%
7.8125 3
0.3%
7.777777778 4
0.5%
7.733333333 2
0.2%

avg_weight
Real number (ℝ)

HIGH CORRELATION 

Distinct497
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.27067
Minimum204.64706
Maximum232.46667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:46.380406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum204.64706
5-th percentile212.5
Q1217.50543
median220.34314
Q3223.60702
95-th percentile227.67566
Maximum232.46667
Range27.819608
Interquartile range (IQR)6.1015828

Descriptive statistics

Standard deviation4.7004351
Coefficient of variation (CV)0.02133936
Kurtosis0.078951899
Mean220.27067
Median Absolute Deviation (MAD)3.0068627
Skewness-0.19923996
Sum190754.4
Variance22.09409
MonotonicityNot monotonic
2024-10-03T16:55:46.423844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219.4736842 7
 
0.8%
218 7
 
0.8%
230.0588235 6
 
0.7%
220.6 6
 
0.7%
224 6
 
0.7%
220.2352941 6
 
0.7%
219 6
 
0.7%
217.4210526 5
 
0.6%
225.8125 5
 
0.6%
220.9333333 5
 
0.6%
Other values (487) 807
93.2%
ValueCountFrequency (%)
204.6470588 1
0.1%
204.8333333 1
0.1%
205.1538462 1
0.1%
207.1578947 1
0.1%
207.5555556 1
0.1%
207.9473684 1
0.1%
208 1
0.1%
208.1818182 1
0.1%
208.1875 1
0.1%
208.2631579 1
0.1%
ValueCountFrequency (%)
232.4666667 1
 
0.1%
231.8 2
 
0.2%
231.75 3
0.3%
231 2
 
0.2%
230.5789474 1
 
0.1%
230.0588235 6
0.7%
229.9 1
 
0.1%
229.5333333 2
 
0.2%
229.3333333 1
 
0.1%
229 3
0.3%

avg_height
Real number (ℝ)

HIGH CORRELATION 

Distinct272
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.828861
Minimum77.24
Maximum80.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:46.466814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum77.24
5-th percentile77.888889
Q178.5
median78.842105
Q379.214286
95-th percentile79.666667
Maximum80.125
Range2.885
Interquartile range (IQR)0.71428571

Descriptive statistics

Standard deviation0.53290108
Coefficient of variation (CV)0.0067602281
Kurtosis-0.23592728
Mean78.828861
Median Absolute Deviation (MAD)0.36591479
Skewness-0.17104557
Sum68265.793
Variance0.28398356
MonotonicityNot monotonic
2024-10-03T16:55:46.511339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 34
 
3.9%
78.8 23
 
2.7%
78.66666667 15
 
1.7%
78.75 14
 
1.6%
79.33333333 12
 
1.4%
79.05882353 12
 
1.4%
79.29411765 11
 
1.3%
78.5 11
 
1.3%
79.15789474 10
 
1.2%
79.2 10
 
1.2%
Other values (262) 714
82.4%
ValueCountFrequency (%)
77.24 1
0.1%
77.26315789 1
0.1%
77.29411765 1
0.1%
77.45 1
0.1%
77.52173913 1
0.1%
77.54545455 1
0.1%
77.55555556 2
0.2%
77.56521739 2
0.2%
77.58823529 1
0.1%
77.6 1
0.1%
ValueCountFrequency (%)
80.125 2
 
0.2%
80.11111111 3
0.3%
80 5
0.6%
79.9375 3
0.3%
79.93333333 3
0.3%
79.9047619 1
 
0.1%
79.88235294 3
0.3%
79.875 1
 
0.1%
79.86666667 2
 
0.2%
79.85714286 3
0.3%

Odds
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14168.995
Minimum175
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:46.562020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum175
5-th percentile450
Q11600
median5000
Q312500
95-th percentile100000
Maximum100000
Range99825
Interquartile range (IQR)10900

Descriptive statistics

Standard deviation24733.847
Coefficient of variation (CV)1.7456316
Kurtosis6.4977692
Mean14168.995
Median Absolute Deviation (MAD)4000
Skewness2.7311353
Sum12270350
Variance6.1176317 × 108
MonotonicityNot monotonic
2024-10-03T16:55:46.614202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10000 79
 
9.1%
5000 64
 
7.4%
100000 55
 
6.4%
15000 54
 
6.2%
4000 52
 
6.0%
6600 44
 
5.1%
20000 42
 
4.8%
3000 39
 
4.5%
50000 37
 
4.3%
1000 37
 
4.3%
Other values (39) 363
41.9%
ValueCountFrequency (%)
175 3
 
0.3%
200 2
 
0.2%
225 8
0.9%
250 3
 
0.3%
275 2
 
0.2%
300 14
1.6%
350 4
 
0.5%
400 1
 
0.1%
450 13
1.5%
500 2
 
0.2%
ValueCountFrequency (%)
100000 55
6.4%
50000 37
4.3%
30000 4
 
0.5%
25000 12
 
1.4%
20000 42
4.8%
15000 54
6.2%
12500 29
 
3.3%
12000 1
 
0.1%
10000 79
9.1%
8000 29
 
3.3%

highest_salary
Real number (ℝ)

HIGH CORRELATION 

Distinct453
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20305152
Minimum5525000
Maximum55761216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:46.664635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5525000
5-th percentile9862207.8
Q113735000
median17545000
Q324080444
95-th percentile40851645
Maximum55761216
Range50236216
Interquartile range (IQR)10345444

Descriptive statistics

Standard deviation9352039.7
Coefficient of variation (CV)0.46057471
Kurtosis1.1477309
Mean20305152
Median Absolute Deviation (MAD)4545000
Skewness1.2606679
Sum1.7584262 × 1010
Variance8.7460646 × 1013
MonotonicityNot monotonic
2024-10-03T16:55:46.714008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000000 14
 
1.6%
12500000 10
 
1.2%
13500000 10
 
1.2%
11000000 10
 
1.2%
14000000 8
 
0.9%
20000000 7
 
0.8%
13000000 7
 
0.8%
26540100 6
 
0.7%
16359805 6
 
0.7%
13520500 6
 
0.7%
Other values (443) 782
90.3%
ValueCountFrequency (%)
5525000 1
 
0.1%
6262347 3
0.3%
6500000 1
 
0.1%
7280000 1
 
0.1%
7350000 3
0.3%
7500000 2
0.2%
7647500 1
 
0.1%
8000000 2
0.2%
8083000 2
0.2%
8175476 1
 
0.1%
ValueCountFrequency (%)
55761216 1
0.1%
51915615 1
0.1%
51415938 2
0.2%
51179021 1
0.1%
49700000 1
0.1%
49205800 2
0.2%
48798677 1
0.1%
48787676 1
0.1%
48728845 1
0.1%
48070014 1
0.1%

median_salary
Real number (ℝ)

Distinct510
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2733017.5
Minimum606702
Maximum11445000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:46.761814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum606702
5-th percentile1316306.6
Q11963609.1
median2500000
Q33181717.6
95-th percentile4990514
Maximum11445000
Range10838298
Interquartile range (IQR)1218108.5

Descriptive statistics

Standard deviation1248773.9
Coefficient of variation (CV)0.45692129
Kurtosis8.9259023
Mean2733017.5
Median Absolute Deviation (MAD)597300
Skewness2.2933643
Sum2.3667932 × 109
Variance1.5594362 × 1012
MonotonicityNot monotonic
2024-10-03T16:55:46.809955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2500000 18
 
2.1%
2000000 15
 
1.7%
3000000 11
 
1.3%
4000000 9
 
1.0%
3250000 8
 
0.9%
1352181 8
 
0.9%
3500000 7
 
0.8%
2020200 5
 
0.6%
3100000 5
 
0.6%
1836090 5
 
0.6%
Other values (500) 775
89.5%
ValueCountFrequency (%)
606702 1
 
0.1%
680937 1
 
0.1%
816481 1
 
0.1%
838464 1
 
0.1%
839209 1
 
0.1%
870318.5 1
 
0.1%
918000 3
0.3%
981084 1
 
0.1%
1020960 3
0.3%
1058622 1
 
0.1%
ValueCountFrequency (%)
11445000 1
0.1%
10130980 1
0.1%
9249960 1
0.1%
9104643.5 1
0.1%
9057971 1
0.1%
8928571.5 1
0.1%
8500000 1
0.1%
8390244 1
0.1%
8250000 1
0.1%
8000000 1
0.1%

total_salary
Real number (ℝ)

HIGH CORRELATION 

Distinct570
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89429834
Minimum41964046
Maximum2.196278 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:46.853545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum41964046
5-th percentile55063049
Q165551946
median74117458
Q31.1131912 × 108
95-th percentile1.6500466 × 108
Maximum2.196278 × 108
Range1.7766376 × 108
Interquartile range (IQR)45767171

Descriptive statistics

Standard deviation34526683
Coefficient of variation (CV)0.38607567
Kurtosis0.62456759
Mean89429834
Median Absolute Deviation (MAD)12759742
Skewness1.2215965
Sum7.7446236 × 1010
Variance1.1920918 × 1015
MonotonicityNot monotonic
2024-10-03T16:55:46.901191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91428140 3
 
0.3%
73440274 3
 
0.3%
48950236 3
 
0.3%
61576901 3
 
0.3%
68415336 3
 
0.3%
65352372 3
 
0.3%
57021755 3
 
0.3%
72315654 3
 
0.3%
68914971 3
 
0.3%
69700359 3
 
0.3%
Other values (560) 836
96.5%
ValueCountFrequency (%)
41964046 1
 
0.1%
45268465 3
0.3%
47560404 2
0.2%
48581972 1
 
0.1%
48950236 3
0.3%
49675131 2
0.2%
51015491 1
 
0.1%
51554083 3
0.3%
53076014 2
0.2%
53225329 3
0.3%
ValueCountFrequency (%)
219627801 1
0.1%
206814776 1
0.1%
205560682 1
0.1%
201620656 1
0.1%
200060815 1
0.1%
191331329 1
0.1%
191189228 1
0.1%
191009786 1
0.1%
189502439 1
0.1%
187028846 1
0.1%

top_players
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
1
429 
0
245 
2
165 
3
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters866
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 429
49.5%
0 245
28.3%
2 165
 
19.1%
3 27
 
3.1%

Length

2024-10-03T16:55:46.944609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T16:55:46.973679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 429
49.5%
0 245
28.3%
2 165
 
19.1%
3 27
 
3.1%

Most occurring characters

ValueCountFrequency (%)
1 429
49.5%
0 245
28.3%
2 165
 
19.1%
3 27
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 866
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 429
49.5%
0 245
28.3%
2 165
 
19.1%
3 27
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 866
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 429
49.5%
0 245
28.3%
2 165
 
19.1%
3 27
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 429
49.5%
0 245
28.3%
2 165
 
19.1%
3 27
 
3.1%

not_top_players
Real number (ℝ)

Distinct18
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.556582
Minimum8
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:47.006048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12
Q114
median16
Q318
95-th percentile24
Maximum25
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4856813
Coefficient of variation (CV)0.21053145
Kurtosis-0.17985397
Mean16.556582
Median Absolute Deviation (MAD)2
Skewness0.59257805
Sum14338
Variance12.149974
MonotonicityNot monotonic
2024-10-03T16:55:47.043307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15 121
14.0%
14 112
12.9%
16 97
11.2%
18 89
10.3%
17 74
8.5%
13 73
8.4%
19 54
6.2%
12 50
 
5.8%
21 32
 
3.7%
20 32
 
3.7%
Other values (8) 132
15.2%
ValueCountFrequency (%)
8 1
 
0.1%
9 2
 
0.2%
10 6
 
0.7%
11 25
 
2.9%
12 50
5.8%
13 73
8.4%
14 112
12.9%
15 121
14.0%
16 97
11.2%
17 74
8.5%
ValueCountFrequency (%)
25 24
 
2.8%
24 21
 
2.4%
23 25
 
2.9%
22 28
 
3.2%
21 32
 
3.7%
20 32
 
3.7%
19 54
6.2%
18 89
10.3%
17 74
8.5%
16 97
11.2%

nb_championships
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9526559
Minimum0
Maximum18
Zeros446
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:47.079412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.6571347
Coefficient of variation (CV)1.8729028
Kurtosis8.1720376
Mean1.9526559
Median Absolute Deviation (MAD)0
Skewness2.8513355
Sum1691
Variance13.374635
MonotonicityNot monotonic
2024-10-03T16:55:47.114649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 446
51.5%
1 125
 
14.4%
2 97
 
11.2%
3 74
 
8.5%
6 29
 
3.3%
17 26
 
3.0%
4 20
 
2.3%
11 18
 
2.1%
5 17
 
2.0%
12 6
 
0.7%
Other values (4) 8
 
0.9%
ValueCountFrequency (%)
0 446
51.5%
1 125
 
14.4%
2 97
 
11.2%
3 74
 
8.5%
4 20
 
2.3%
5 17
 
2.0%
6 29
 
3.3%
9 3
 
0.3%
10 2
 
0.2%
11 18
 
2.1%
ValueCountFrequency (%)
18 2
 
0.2%
17 26
3.0%
16 1
 
0.1%
12 6
 
0.7%
11 18
2.1%
10 2
 
0.2%
9 3
 
0.3%
6 29
3.3%
5 17
2.0%
4 20
2.3%

nb_champ_past_4y
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
773 
1
 
72
2
 
20
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters866
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 773
89.3%
1 72
 
8.3%
2 20
 
2.3%
3 1
 
0.1%

Length

2024-10-03T16:55:47.152676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T16:55:47.180406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 773
89.3%
1 72
 
8.3%
2 20
 
2.3%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 773
89.3%
1 72
 
8.3%
2 20
 
2.3%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 866
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 773
89.3%
1 72
 
8.3%
2 20
 
2.3%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 866
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 773
89.3%
1 72
 
8.3%
2 20
 
2.3%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 773
89.3%
1 72
 
8.3%
2 20
 
2.3%
3 1
 
0.1%

nb_po_apperence
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.47806
Minimum1
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:47.216873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median25
Q333
95-th percentile49
Maximum61
Range60
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.194442
Coefficient of variation (CV)0.60458325
Kurtosis-0.80783038
Mean23.47806
Median Absolute Deviation (MAD)11
Skewness0.21580097
Sum20332
Variance201.48218
MonotonicityNot monotonic
2024-10-03T16:55:47.262078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 67
 
7.7%
33 46
 
5.3%
42 42
 
4.8%
29 39
 
4.5%
25 36
 
4.2%
32 31
 
3.6%
4 30
 
3.5%
31 25
 
2.9%
18 24
 
2.8%
8 24
 
2.8%
Other values (51) 502
58.0%
ValueCountFrequency (%)
1 19
 
2.2%
2 11
 
1.3%
3 12
 
1.4%
4 30
3.5%
5 67
7.7%
6 10
 
1.2%
7 8
 
0.9%
8 24
 
2.8%
9 16
 
1.8%
10 14
 
1.6%
ValueCountFrequency (%)
61 2
 
0.2%
60 1
 
0.1%
59 1
 
0.1%
58 1
 
0.1%
57 1
 
0.1%
56 1
 
0.1%
55 1
 
0.1%
54 1
 
0.1%
53 13
1.5%
52 2
 
0.2%

winner
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
838 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters866
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 838
96.8%
1 28
 
3.2%

Length

2024-10-03T16:55:47.303799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T16:55:47.333164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 838
96.8%
1 28
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 838
96.8%
1 28
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 866
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 838
96.8%
1 28
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 866
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 838
96.8%
1 28
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 838
96.8%
1 28
 
3.2%

ranking
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)1.8%
Missing30
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean7.9736842
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-10-03T16:55:47.362294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q312
95-th percentile15
Maximum15
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.3158836
Coefficient of variation (CV)0.54126593
Kurtosis-1.206034
Mean7.9736842
Median Absolute Deviation (MAD)4
Skewness0.0071665579
Sum6666
Variance18.626852
MonotonicityNot monotonic
2024-10-03T16:55:47.400368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
10 56
 
6.5%
8 56
 
6.5%
9 56
 
6.5%
5 56
 
6.5%
4 56
 
6.5%
1 56
 
6.5%
6 56
 
6.5%
3 56
 
6.5%
2 56
 
6.5%
7 56
 
6.5%
Other values (5) 276
31.9%
ValueCountFrequency (%)
1 56
6.5%
2 56
6.5%
3 56
6.5%
4 56
6.5%
5 56
6.5%
6 56
6.5%
7 56
6.5%
8 56
6.5%
9 56
6.5%
10 56
6.5%
ValueCountFrequency (%)
15 55
6.4%
14 55
6.4%
13 55
6.4%
12 55
6.4%
11 56
6.5%
10 56
6.5%
9 56
6.5%
8 56
6.5%
7 56
6.5%
6 56
6.5%

Interactions

2024-10-03T16:55:45.348463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:38.545715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.206440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.718481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.682260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.155144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.632446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.121019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.581318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.053145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.569028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.070296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.564976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.382500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:38.660739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.244980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.756734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.723043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.194134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.668272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.156898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.622991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.090617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.612098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.111698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.600527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.417860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:38.744723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.284747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.795089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.764292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.232147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.706155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.194002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.662689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.129350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.652402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.155453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.950224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.450405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:38.810458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.326102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.318532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.797596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.277148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.742309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.229570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.700768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.165395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.689266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.196212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.988186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.482684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:38.853508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.364748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.356184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.830349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.311239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.778394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.262400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.737324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.199872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.725877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.234872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.025451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.514198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:38.893598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.405022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.395997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.862195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.346039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.814676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.295276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.773210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.233668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.762078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.275640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.061353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.546349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:38.933947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.444960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.431683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.895370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.381676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.849169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.327177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.807338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.267509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.797847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.313951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.098521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.576799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:38.971727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.483036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.465783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.929815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.416368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.895103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.357307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.840563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.301903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.831203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.350193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.135848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.610655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.010813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.522473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.500615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.965091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.450240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.932133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.388077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.873242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.339682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.866614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.386364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.171575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.643831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.053399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.564713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.538740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.004343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.487856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.974034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.421783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.909618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.379581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.921267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.423715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.209289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.677952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.091911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.605437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.575272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.041000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.524887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.014092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.463382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.946830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.433570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.959742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.459611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.245177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.715383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.132049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.644065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.613061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.080002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.561260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.051537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.503250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.983496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.484695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.997751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.495097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.280829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.752355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.169352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:39.682389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:40.647992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.117276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:41.597818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.086576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:42.543256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.018237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:43.527214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.035419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:44.530027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T16:55:45.313999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-03T16:55:47.438992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
OddsSeasonavg_ageavg_expavg_heightavg_weightconferencehighest_salarymedian_salarynb_champ_past_4ynb_championshipsnb_po_apperencenot_top_playersrankingteamteam_full_nametop_playerstotal_salarywinner
Odds1.0000.094-0.363-0.371-0.053-0.0130.184-0.1670.0620.089-0.258-0.1440.1800.4350.5100.5100.108-0.1280.074
Season0.0941.000-0.246-0.166-0.403-0.3570.0000.6160.0360.0000.0760.1300.4050.0040.0000.0000.1540.6930.000
avg_age-0.363-0.2461.0000.9130.1010.1390.1720.050-0.0620.3330.1780.049-0.284-0.4480.2540.2540.2830.0290.245
avg_exp-0.371-0.1660.9131.0000.1020.1570.1100.1210.0300.2950.1790.077-0.311-0.4780.2500.2500.1630.1170.290
avg_height-0.053-0.4030.1010.1021.0000.5820.000-0.3250.0940.1380.042-0.017-0.282-0.0640.1930.1930.124-0.3230.209
avg_weight-0.013-0.3570.1390.1570.5821.0000.069-0.3370.0610.049-0.085-0.045-0.344-0.1250.1910.1910.183-0.3600.144
conference0.1840.0000.1720.1100.0000.0691.0000.0570.0000.0650.4790.2400.0610.0000.9810.9810.0650.0140.000
highest_salary-0.1670.6160.0500.121-0.325-0.3370.0571.000-0.0910.1840.2100.1550.209-0.2460.2190.2190.3500.8640.102
median_salary0.0620.036-0.0620.0300.0940.0610.000-0.0911.0000.041-0.044-0.060-0.393-0.0340.1260.1260.1280.0490.146
nb_champ_past_4y0.0890.0000.3330.2950.1380.0490.0650.1840.0411.0000.3490.2740.0740.2180.3390.3390.1610.1850.544
nb_championships-0.2580.0760.1780.1790.042-0.0850.4790.210-0.0440.3491.0000.610-0.044-0.2200.7470.7470.1050.1890.330
nb_po_apperence-0.1440.1300.0490.077-0.017-0.0450.2400.155-0.0600.2740.6101.0000.041-0.0820.6190.6190.1500.1520.274
not_top_players0.1800.405-0.284-0.311-0.282-0.3440.0610.209-0.3930.074-0.0440.0411.0000.2510.1790.1790.2670.3480.058
ranking0.4350.004-0.448-0.478-0.064-0.1250.000-0.246-0.0340.218-0.220-0.0820.2511.0000.2440.2440.232-0.2070.372
team0.5100.0000.2540.2500.1930.1910.9810.2190.1260.3390.7470.6190.1790.2441.0001.0000.2830.1700.255
team_full_name0.5100.0000.2540.2500.1930.1910.9810.2190.1260.3390.7470.6190.1790.2441.0001.0000.2830.1700.255
top_players0.1080.1540.2830.1630.1240.1830.0650.3500.1280.1610.1050.1500.2670.2320.2830.2831.0000.2140.234
total_salary-0.1280.6930.0290.117-0.323-0.3600.0140.8640.0490.1850.1890.1520.348-0.2070.1700.1700.2141.0000.097
winner0.0740.0000.2450.2900.2090.1440.0000.1020.1460.5440.3300.2740.0580.3720.2550.2550.2340.0971.000

Missing values

2024-10-03T16:55:45.809805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-03T16:55:45.907940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Seasonteamteam_full_nameconferenceavg_ageavg_expavg_weightavg_heightOddshighest_salarymedian_salarytotal_salarytop_playersnot_top_playersnb_championshipsnb_champ_past_4ynb_po_apperencewinnerranking
02025ATLAtlanta HawksEAST25.3000003.700000217.10526378.95000015000430319406059520.016952907011400360NaN
12024ATLAtlanta HawksEAST25.7894744.368421210.63157978.26315815000400642202753441.01572593611210036010.0
22023ATLAtlanta HawksEAST24.7000003.350000211.05000078.50000015000370965002755015.5148633740119003608.0
32022ATLAtlanta HawksEAST25.9166674.125000213.62500078.25000015000230000002659560.0133731629025003509.0
42021ATLAtlanta HawksEAST25.6666674.388889213.16666777.83333315000195000004612500.0115195055018003405.0
52020ATLAtlanta HawksEAST25.7142864.142857216.66666778.71428615000251025112564753.01132867431210033014.0
62019ATLAtlanta HawksEAST25.0000003.181818219.13636479.13636415000255342532275020.01070764351230033012.0
72018ATLAtlanta HawksEAST25.5000002.681818208.18181878.36363615000169101131662500.0995743210250033015.0
82017ATLAtlanta HawksEAST28.2000005.900000222.05000078.80000015000231802752299942.596007250218003305.0
92016ATLAtlanta HawksEAST27.3529415.294118218.00000078.58823515000190000002435000.071453126115003204.0
Seasonteamteam_full_nameconferenceavg_ageavg_expavg_weightavg_heightOddshighest_salarymedian_salarytotal_salarytop_playersnot_top_playersnb_championshipsnb_champ_past_4ynb_po_apperencewinnerranking
8562007ORLOrlando MagicEAST26.0000004.733333217.73333378.66666715000169015003224000.06144710811500908.0
8572007PHIPhiladelphia 76ersEAST26.2777784.444444216.50000079.05555612000176000002800000.069140163116202709.0
8582007PHOPhoenix SunsWEST29.0714296.357143221.35714379.071429600150700001870501.065841120114002702.0
8592007PORPortland Trail BlazersWEST24.7058822.705882218.76470679.11764750000120000002780160.0750263860171030013.0
8602007SACSacramento KingsWEST27.2000005.466667222.93333378.6666673500125000001460460.0642966110180030011.0
8612007SASSan Antonio SpursWEST29.4375006.125000219.06250079.500000450174296723000000.065654320114423013.0
8622007SEASeattle SuperSonicsWEST25.7500003.625000223.87500078.87500010000146115701958640.056986831116104014.0
8632007TORToronto RaptorsEAST25.8235292.882353219.00000078.7058821000072800002739733.55101549101800404.0
8642007UTAUtah JazzWEST24.9333332.933333229.33333378.8666678000123640002395200.062364364017002105.0
8652007WASWashington WizardsEAST26.1250004.062500227.81250079.8750006500151016251803600.06273370511600307.0

Duplicate rows

Most frequently occurring

Seasonteamteam_full_nameconferenceavg_ageavg_expavg_weightavg_heightOddshighest_salarymedian_salarytotal_salarytop_playersnot_top_playersnb_championshipsnb_champ_past_4ynb_po_apperencewinnerranking# duplicates
72010MINMinnesota TimberwolvesWEST24.9375003.500000227.50000079.37500020000120000002969280.062253360017005015.03
122011MINMinnesota TimberwolvesWEST23.6470592.588235218.11764778.52941220000115305923415140.053461123115005015.03
172012MINMinnesota TimberwolvesWEST24.7333333.733333224.60000078.8000002000062623474300000.056844339015005012.03
02008CHACharlotte BobcatsEAST27.3333335.200000217.53333378.80000012500111111102376000.053745468114008012.02
12008MINMinnesota TimberwolvesWEST25.9375004.312500226.18750079.00000015000116666662562426.069043895019005013.02
22009CHACharlotte BobcatsEAST25.8333333.958333226.25000079.3333331250095375002661027.062866926017008010.02
32009MINMinnesota TimberwolvesWEST26.4705884.352941224.94117679.00000015000110000002989620.066445144016005011.02
42010ATLAtlanta HawksEAST26.5000005.642857227.35714379.3571435000149767543403820.065883642113002603.02
52010GSWGolden State WarriorsWEST26.2500004.100000214.80000078.80000012500110000002671440.0653523720191033013.02
62010HOUHouston RocketsWEST25.9047623.047619222.14285779.0476194000163783252300000.068914971118202509.02